Nate Silver's book "The Signal and the Noise"

, Star Tribune

Business Bookshelf: "The Signal and the Noise"

  • October 13, 2012 - 5:30 PM


Nate Silver, Penguin Press, 534 pages, $27.95

Humility and punditry are two characteristics not often found in the same person -- or the same book. But they co-exist more or less happily in "The Signal and the Noise," Nate Silver's engaging if occasionally shambling book on the fine art of prediction.

And it is an art, as Silver -- the proprietor of the New York Times' FiveThirtyEight political blog -- makes clear. When it comes to forecasting, he shows that knowing what to ignore is at least as important as what to consider. Also vital, he argues, is being willing to embrace the uncertainties inherent in trying to foretell the future. And if that means the resulting prediction is a little less sure of itself than we're used to hearing from our expert class, so be it.

Silver first came to prominence among a subset of baseball statheads with Pecota, a system for projecting players' performance. It had some notable successes, along with notable misses. Silver, to his credit, not only talks about the errant predictions but draws the larger lesson: "The key to making a good forecast," he says, "is not in limiting yourself to quantitative information. Rather, it's having a good process for weighing the information appropriately."

That task is made infinitely more difficult by the vast amounts of information available. The trick is in separating the knowable from the unknowable and the relevant from the irrelevant, and in recognizing the difference between correlation and causation. Ice-cream sales and forest fires both peak in the summer, he notes. "But there is no causation; you don't light a patch of the Montana brush on fire when you buy a pint of Haagen-Dazs."

Silver displays a knack not just for mining data but for explaining his thinking in an accessible way. It's nice to come across someone as insightful as he is who doesn't seem completely dazzled by his own brilliance. "Our bias is to think we are better at prediction than we really are," he concludes, hoping for a future in which we're "a little more modest about our forecasting abilities, and a little less likely to repeat our mistakes."


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